Reflection AI's 501B Beam Takes On China's Open Models

Reflection AI's new 501B open model, Beam, against China's open models: what its own benchmark table shows, where its efficiency claim comes from, and whether you should wait for it. Reflection AI announced Beam on October 5, 2026: a 501-billion-parameter mixture-of-experts model with 23 billion parameters active per token, for coding, reasoning and agents. As of October 16 there is no download yet; Reflection says the weights arrive later this month under Apache 2.0. The only comparison is Reflection's own table. On Terminal-Bench v2.1 Beam scores 80.1, against 86.6 to 90.6 for the newest Chinese open models; it roughly ties June's GLM-5.2 and wins only a handful of about 50 shared rows against GLM-5.3, Kimi K3, Qwen 3.8-Max and DeepSeek V4.1 Flash. In that table Beam beats the two other American open models, Thinking Machines' Inkling and NVIDIA's Nemotron 3 Ultra. Artificial Analysis's independent ranking (October 15) puts the best American open model, Meta's Muse Spark Open, at 39; Reflection's table doesn't include it, and Artificial Analysis hasn't scored Beam yet. Reflection's pitch is efficiency: it estimates 3 to 4 times less compute than GLM-5.2, from fewer active parameters and shorter answers. The estimate covers writing only, not reading the prompt, attention or serving, and the API is a free beta with daily limits and no price after the beta. To run it at home, by our math Beam needs about 300 GB at 4 bits: a 512 GB Mac Studio (coming late October, no price), two linked 256 GB Macs at about $19,000, or four RTX PRO 6000 cards at $64,000 before a motherboard or power supply. DeepSeek V4.1 Flash, released September 10 under MIT, already fits the same machines through antirez's DwarfStar engine and beats Beam on most rows of Reflection's table; its llama.cpp pull request is still waiting for review. Verdict: don't wait for Beam and don't buy hardware for it. Watch it if you can't run Chinese-lab models, and before downloading 300 GB check independent scores, the real 4-bit file sizes and llama.cpp/MLX support. Chapters: 0:00 Intro 1:30 The Scores 3:17 The Efficiency 5:55 Running It 9:08 Who It's For 10:27 Verdict Tools & resources mentioned: - Reflection AI: Introducing Beam: https://reflection.ai/blog/introducing-beam - Reflection on Hugging Face: https://huggingface.co/reflection - Z.ai GLM-5.2: https://huggingface.co/zai-org/GLM-5.2 - DeepSeek V4.1 Flash: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash - Thinking Machines Inkling: https://huggingface.co/thinkingmachines/Inkling - NVIDIA Nemotron 3 Ultra: https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 - Artificial Analysis open-weights ranking: https://artificialanalysis.ai/models/open-source - Unsloth GLM-5.2 guide: https://unsloth.ai/docs/models/glm-5.2 - DwarfStar (antirez): DeepSeek V4.1 Flash GGUF: https://huggingface.co/antirez/deepseek-v4.1-flash-gguf - llama.cpp pull request #28696: https://github.com/ggml-org/llama.cpp/pull/28696 - llama.cpp: https://github.com/ggml-org/llama.cpp - MLX: https://github.com/ml-explore/mlx - Ollama: https://ollama.com About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #ReflectionAI #OpenWeights #LocalAI #DeepSeek #LLM